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<meta property="og:description" content="圆柱体模型分割🔍 1. 整体流程该程序实现了一个典型的 两级 RANSAC 分割流程：  先用 RANSAC 拟合一个平面模型（代表桌面）； 去除平面上的点后，在剩下的点云中拟合一个圆柱模型（可能代表杯子或瓶子）；  这是点云中常见的“场景分割”策略：先分离大平面背景，再识别特定物体。  🧩 2. 核心组件说明   组件 作用    PassThrough 去除 z 轴范围外的噪声或无关点（">
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<h1 id="圆柱体模型分割"><a href="#圆柱体模型分割" class="headerlink" title="圆柱体模型分割"></a>圆柱体模型分割</h1><h2 id="🔍-1-整体流程"><a href="#🔍-1-整体流程" class="headerlink" title="🔍 1. 整体流程"></a>🔍 1. <strong>整体流程</strong></h2><p>该程序实现了一个典型的 <strong>两级 RANSAC 分割流程</strong>：</p>
<ol>
<li>先用 RANSAC 拟合一个<strong>平面模型</strong>（代表桌面）；</li>
<li>去除平面上的点后，在剩下的点云中拟合一个<strong>圆柱模型</strong>（可能代表杯子或瓶子）；</li>
</ol>
<p>这是点云中常见的“场景分割”策略：先分离大平面背景，再识别特定物体。</p>
<hr>
<h2 id="🧩-2-核心组件说明"><a href="#🧩-2-核心组件说明" class="headerlink" title="🧩 2. 核心组件说明"></a>🧩 2. <strong>核心组件说明</strong></h2><table>
<thead>
<tr>
<th>组件</th>
<th>作用</th>
</tr>
</thead>
<tbody><tr>
<td><code>PassThrough</code></td>
<td>去除 z 轴范围外的噪声或无关点（如天花板、地面以下），提高后续处理效率</td>
</tr>
<tr>
<td><code>NormalEstimation</code></td>
<td>为每个点计算法向量，用于支持基于法线的模型拟合（如 SACMODEL_NORMAL_PLANE）</td>
</tr>
<tr>
<td><code>KdTree</code></td>
<td>加速邻域搜索，提升法线估计和分割速度</td>
</tr>
<tr>
<td><code>SACSegmentationFromNormals</code></td>
<td>支持法线信息的 RANSAC 分割器，能更准确地拟合几何模型</td>
</tr>
<tr>
<td><code>ExtractIndices</code></td>
<td>根据索引提取或剔除点，常用于分离不同物体</td>
</tr>
</tbody></table>
<hr>
<h2 id="⚙️-3-平面分割参数"><a href="#⚙️-3-平面分割参数" class="headerlink" title="⚙️ 3. 平面分割参数"></a>⚙️ 3. <strong>平面分割参数</strong></h2><figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">seg.<span class="built_in">setModelType</span> (pcl::SACMODEL_NORMAL_PLANE);</span><br><span class="line">seg.<span class="built_in">setMethodType</span> (pcl::SAC_RANSAC);</span><br><span class="line">seg.<span class="built_in">setDistanceThreshold</span> (<span class="number">0.03</span>); <span class="comment">// 3cm 内认为是平面点</span></span><br><span class="line">seg.<span class="built_in">setMaxIterations</span> (<span class="number">100</span>);      <span class="comment">// 迭代较少，因为平面容易拟合</span></span><br></pre></td></tr></table></figure>
<ul>
<li>利用了法线一致性（<code>SACMODEL_NORMAL_PLANE</code>），比普通平面更鲁棒。</li>
<li>距离阈值小 → 精度高但可能漏检。</li>
</ul>
<p>–</p>
<h2 id="📦-4-圆柱分割难点与对策"><a href="#📦-4-圆柱分割难点与对策" class="headerlink" title="📦 4. 圆柱分割难点与对策"></a>📦 4. <strong>圆柱分割难点与对策</strong></h2><figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">seg.<span class="built_in">setModelType</span> (pcl::SACMODEL_CYLINDER);</span><br><span class="line">seg.<span class="built_in">setMaxIterations</span> (<span class="number">10000</span>);           <span class="comment">// 增加迭代次数</span></span><br><span class="line">seg.<span class="built_in">setDistanceThreshold</span> (<span class="number">0.05</span>);        <span class="comment">// 放宽距离阈值</span></span><br><span class="line">seg.<span class="built_in">setRadiusLimits</span> (<span class="number">0</span>, <span class="number">0.1</span>);           <span class="comment">// 限定半径范围（0~10cm）</span></span><br></pre></td></tr></table></figure>
<ul>
<li>圆柱拟合比平面困难（自由度更高），所以需要：<ul>
<li>更多迭代；</li>
<li>更宽松的距离阈值；</li>
<li>设置合理的半径限制以避免误匹配；</li>
</ul>
</li>
<li>使用法线权重 (<code>setNormalDistanceWeight</code>) 提高准确性。</li>
</ul>
<hr>
<h2 id="💾-5-输出结果"><a href="#💾-5-输出结果" class="headerlink" title="💾 5. 输出结果"></a>💾 5. <strong>输出结果</strong></h2><ul>
<li><code>table_scene_mug_stereo_textured_plane.pcd</code>：检测出的桌面点云；</li>
<li><code>table_scene_mug_stereo_textured_cylinder.pcd</code>：可能是杯子的圆柱形物体；</li>
<li>若未找到圆柱，则提示无法识别。</li>
</ul>
<hr>
<h2 id="📌-6-应用场景"><a href="#📌-6-应用场景" class="headerlink" title="📌 6. 应用场景"></a>📌 6. <strong>应用场景</strong></h2><p>适用于机器人视觉、物体识别、场景解析等任务，例如：</p>
<ul>
<li>服务机器人识别桌上的杯子；</li>
<li>工业检测中识别轴类零件；</li>
<li>AR&#x2F;VR 中理解环境结构。</li>
</ul>
<hr>
<h2 id="🛠️-7-可改进方向"><a href="#🛠️-7-可改进方向" class="headerlink" title="🛠️ 7. 可改进方向"></a>🛠️ 7. <strong>可改进方向</strong></h2><table>
<thead>
<tr>
<th>问题</th>
<th>改进建议</th>
</tr>
</thead>
<tbody><tr>
<td>只检测一个平面和一个圆柱</td>
<td>可循环多次提取多个平面或圆柱</td>
</tr>
<tr>
<td>参数固定</td>
<td>可根据点云密度动态调整阈值</td>
</tr>
<tr>
<td>缺少可视化</td>
<td>加入 PCLVisualizer 显示结果</td>
</tr>
<tr>
<td>无颜色信息</td>
<td>若使用 RGB 点类型（如 PointXYZRGB），可结合颜色分割</td>
</tr>
</tbody></table>
<hr>
<hr>
<h2 id="代码实现"><a href="#代码实现" class="headerlink" title="代码实现"></a>代码实现</h2><figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span 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class="line">120</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/ModelCoefficients.h&gt;</span>           <span class="comment">// 存储模型参数（如平面、圆柱等的系数）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/io/pcd_io.h&gt;</span>                   <span class="comment">// PCD 文件读写功能</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/point_types.h&gt;</span>                 <span class="comment">// 点云数据类型定义，如 PointXYZ</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/filters/extract_indices.h&gt;</span>     <span class="comment">// 提取或移除指定索引的点</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/filters/passthrough.h&gt;</span>         <span class="comment">// 通过滤波器，去除无效或超出范围的点</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/features/normal_3d.h&gt;</span>          <span class="comment">// 法线估计模块</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/sample_consensus/method_types.h&gt;</span> <span class="comment">// 随机采样一致性方法（如 RANSAC）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/sample_consensus/model_types.h&gt;</span>  <span class="comment">// 模型类型（如平面、圆柱）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/segmentation/sac_segmentation.h&gt;</span> <span class="comment">// 基于采样一致性的分割算法</span></span></span><br><span class="line"></span><br><span class="line"><span class="keyword">typedef</span> pcl::PointXYZ PointT;                <span class="comment">// 定义点类型别名：只含 x, y, z 坐标</span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="type">int</span> <span class="title">main</span> <span class="params">(<span class="type">int</span> argc, <span class="type">char</span>** argv)</span></span></span><br><span class="line"><span class="function"></span>&#123;</span><br><span class="line">  <span class="comment">// 所需的对象声明</span></span><br><span class="line">  pcl::PCDReader reader;                      <span class="comment">// 用于读取 .pcd 文件</span></span><br><span class="line">  pcl::PassThrough&lt;PointT&gt; pass;             <span class="comment">// 通过滤波器对象</span></span><br><span class="line">  pcl::NormalEstimation&lt;PointT, pcl::Normal&gt; ne; <span class="comment">// 法线估计对象</span></span><br><span class="line">  pcl::SACSegmentationFromNormals&lt;PointT, pcl::Normal&gt; seg; <span class="comment">// 支持法线的 RANSAC 分割对象</span></span><br><span class="line">  pcl::PCDWriter writer;                      <span class="comment">// 用于写入 .pcd 文件</span></span><br><span class="line">  pcl::ExtractIndices&lt;PointT&gt; extract;        <span class="comment">// 提取索引对应的点（用于分离点云）</span></span><br><span class="line">  pcl::ExtractIndices&lt;pcl::Normal&gt; extract_normals; <span class="comment">// 提取法线中对应索引的法线</span></span><br><span class="line">  pcl::search::KdTree&lt;PointT&gt;::<span class="function">Ptr <span class="title">tree</span> <span class="params">(<span class="keyword">new</span> pcl::search::KdTree&lt;PointT&gt; ())</span></span>; <span class="comment">// KdTree 加速搜索结构</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 数据容器</span></span><br><span class="line">  pcl::PointCloud&lt;PointT&gt;::<span class="function">Ptr <span class="title">cloud</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;PointT&gt;)</span></span>;           <span class="comment">// 原始点云</span></span><br><span class="line">  pcl::PointCloud&lt;PointT&gt;::<span class="function">Ptr <span class="title">cloud_filtered</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;PointT&gt;)</span></span>;  <span class="comment">// 滤波后的点云</span></span><br><span class="line">  pcl::PointCloud&lt;pcl::Normal&gt;::<span class="function">Ptr <span class="title">cloud_normals</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;pcl::Normal&gt;)</span></span>; <span class="comment">// 法线点云</span></span><br><span class="line">  pcl::PointCloud&lt;PointT&gt;::<span class="function">Ptr <span class="title">cloud_filtered2</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;PointT&gt;)</span></span>; <span class="comment">// 去除平面后剩余的点云</span></span><br><span class="line">  pcl::PointCloud&lt;pcl::Normal&gt;::<span class="function">Ptr <span class="title">cloud_normals2</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;pcl::Normal&gt;)</span></span>; <span class="comment">// 剩余点的法线</span></span><br><span class="line">  pcl::<span class="function">ModelCoefficients::Ptr <span class="title">coefficients_plane</span> <span class="params">(<span class="keyword">new</span> pcl::ModelCoefficients)</span>, <span class="comment">// 平面模型参数</span></span></span><br><span class="line"><span class="function">                                 <span class="title">coefficients_cylinder</span> <span class="params">(<span class="keyword">new</span> pcl::ModelCoefficients)</span></span>; <span class="comment">// 圆柱模型参数</span></span><br><span class="line">  pcl::<span class="function">PointIndices::Ptr <span class="title">inliers_plane</span> <span class="params">(<span class="keyword">new</span> pcl::PointIndices)</span>,              <span class="comment">// 平面内点索引</span></span></span><br><span class="line"><span class="function">                         <span class="title">inliers_cylinder</span> <span class="params">(<span class="keyword">new</span> pcl::PointIndices)</span></span>;            <span class="comment">// 圆柱内点索引</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 读取点云数据</span></span><br><span class="line">  reader.<span class="built_in">read</span> (<span class="string">&quot;../table_scene_mug_stereo_textured.pcd&quot;</span>, *cloud);</span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;PointCloud has: &quot;</span> &lt;&lt; cloud-&gt;points.<span class="built_in">size</span> () &lt;&lt; <span class="string">&quot; data points.&quot;</span> &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 使用直通滤波器去除 z 轴方向上的无效点（如太远或 NaN）</span></span><br><span class="line">  pass.<span class="built_in">setInputCloud</span> (cloud);                  <span class="comment">// 输入原始点云</span></span><br><span class="line">  pass.<span class="built_in">setFilterFieldName</span> (<span class="string">&quot;z&quot;</span>);               <span class="comment">// 对 z 字段进行过滤</span></span><br><span class="line">  pass.<span class="built_in">setFilterLimits</span> (<span class="number">0</span>, <span class="number">1.5</span>);               <span class="comment">// 保留 z ∈ [0, 1.5] 的点</span></span><br><span class="line">  pass.<span class="built_in">filter</span> (*cloud_filtered);               <span class="comment">// 执行滤波，结果存入 cloud_filtered</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;PointCloud after filtering has: &quot;</span> &lt;&lt; cloud_filtered-&gt;points.<span class="built_in">size</span> () &lt;&lt; <span class="string">&quot; data points.&quot;</span> &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 估计点云法线</span></span><br><span class="line">  ne.<span class="built_in">setSearchMethod</span> (tree);                   <span class="comment">// 设置搜索方式为 KdTree</span></span><br><span class="line">  ne.<span class="built_in">setInputCloud</span> (cloud_filtered);           <span class="comment">// 输入滤波后的点云</span></span><br><span class="line">  ne.<span class="built_in">setKSearch</span> (<span class="number">50</span>);                          <span class="comment">// 使用最近的 50 个邻居来估计法线</span></span><br><span class="line">  ne.<span class="built_in">compute</span> (*cloud_normals);                 <span class="comment">// 计算法线并存储到 cloud_normals</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 设置平面分割器参数</span></span><br><span class="line">  seg.<span class="built_in">setOptimizeCoefficients</span> (<span class="literal">true</span>);          <span class="comment">// 启用系数优化（最小二乘拟合）</span></span><br><span class="line">  seg.<span class="built_in">setModelType</span> (pcl::SACMODEL_NORMAL_PLANE); <span class="comment">// 使用带法线的平面模型</span></span><br><span class="line">  seg.<span class="built_in">setNormalDistanceWeight</span> (<span class="number">0.1</span>);           <span class="comment">// 法线偏差的权重（平衡距离和法线一致性）</span></span><br><span class="line">  seg.<span class="built_in">setMethodType</span> (pcl::SAC_RANSAC);         <span class="comment">// 使用 RANSAC 算法</span></span><br><span class="line">  seg.<span class="built_in">setMaxIterations</span> (<span class="number">100</span>);                  <span class="comment">// 最大迭代次数</span></span><br><span class="line">  seg.<span class="built_in">setDistanceThreshold</span> (<span class="number">0.03</span>);             <span class="comment">// 到模型的距离阈值（3cm）</span></span><br><span class="line">  seg.<span class="built_in">setInputCloud</span> (cloud_filtered);          <span class="comment">// 输入滤波后的点云</span></span><br><span class="line">  seg.<span class="built_in">setInputNormals</span> (cloud_normals);         <span class="comment">// 输入对应的法线</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 执行平面分割</span></span><br><span class="line">  seg.<span class="built_in">segment</span> (*inliers_plane, *coefficients_plane); <span class="comment">// 输出内点索引和模型参数</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;Plane coefficients: &quot;</span> &lt;&lt; *coefficients_plane &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 提取检测到的平面点云（即桌面上的点）</span></span><br><span class="line">  extract.<span class="built_in">setInputCloud</span> (cloud_filtered);      <span class="comment">// 输入是滤波后的点云</span></span><br><span class="line">  extract.<span class="built_in">setIndices</span> (inliers_plane);          <span class="comment">// 指定要提取的索引（平面内点）</span></span><br><span class="line">  extract.<span class="built_in">setNegative</span> (<span class="literal">false</span>);                 <span class="comment">// 提取内点（而非外点）</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 将提取的平面保存为新文件</span></span><br><span class="line">  pcl::PointCloud&lt;PointT&gt;::<span class="function">Ptr <span class="title">cloud_plane</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;PointT&gt; ())</span></span>;</span><br><span class="line">  extract.<span class="built_in">filter</span> (*cloud_plane);               <span class="comment">// 执行提取</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;PointCloud representing the planar component: &quot;</span> &lt;&lt; cloud_plane-&gt;points.<span class="built_in">size</span> () &lt;&lt; <span class="string">&quot; data points.&quot;</span> &lt;&lt; std::endl;</span><br><span class="line">  writer.<span class="built_in">write</span> (<span class="string">&quot;table_scene_mug_stereo_textured_plane.pcd&quot;</span>, *cloud_plane, <span class="literal">false</span>); <span class="comment">// 保存为 PCD 文件</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 现在移除平面点，提取剩下的点（可能是物体）</span></span><br><span class="line">  extract.<span class="built_in">setNegative</span> (<span class="literal">true</span>);                  <span class="comment">// 改为提取“非平面”点（即外点）</span></span><br><span class="line">  extract.<span class="built_in">filter</span> (*cloud_filtered2);           <span class="comment">// 结果存入 cloud_filtered2</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 同样地，从原始法线中提取非平面部分的法线</span></span><br><span class="line">  extract_normals.<span class="built_in">setNegative</span> (<span class="literal">true</span>);          <span class="comment">// 提取非平面法线</span></span><br><span class="line">  extract_normals.<span class="built_in">setInputCloud</span> (cloud_normals); <span class="comment">// 输入原始法线</span></span><br><span class="line">  extract_normals.<span class="built_in">setIndices</span> (inliers_plane);  <span class="comment">// 使用平面索引</span></span><br><span class="line">  extract_normals.<span class="built_in">filter</span> (*cloud_normals2);    <span class="comment">// 得到剩余点的法线 cloud_normals2</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 开始对剩余点云进行圆柱模型分割</span></span><br><span class="line">  seg.<span class="built_in">setOptimizeCoefficients</span> (<span class="literal">true</span>);          <span class="comment">// 仍启用优化</span></span><br><span class="line">  seg.<span class="built_in">setModelType</span> (pcl::SACMODEL_CYLINDER);   <span class="comment">// 模型设为圆柱</span></span><br><span class="line">  seg.<span class="built_in">setMethodType</span> (pcl::SAC_RANSAC);         <span class="comment">// 仍用 RANSAC</span></span><br><span class="line">  seg.<span class="built_in">setNormalDistanceWeight</span> (<span class="number">0.1</span>);           <span class="comment">// 法线权重保持一致</span></span><br><span class="line">  seg.<span class="built_in">setMaxIterations</span> (<span class="number">10000</span>);                <span class="comment">// 圆柱更难拟合，增加迭代次数</span></span><br><span class="line">  seg.<span class="built_in">setDistanceThreshold</span> (<span class="number">0.05</span>);             <span class="comment">// 距离阈值放宽至 5cm（圆柱表面可能不规则）</span></span><br><span class="line">  seg.<span class="built_in">setRadiusLimits</span> (<span class="number">0</span>, <span class="number">0.1</span>);                <span class="comment">// 半径限制：0 到 10cm（排除过大或过小的圆柱）</span></span><br><span class="line">  seg.<span class="built_in">setInputCloud</span> (cloud_filtered2);         <span class="comment">// 输入是非平面点云</span></span><br><span class="line">  seg.<span class="built_in">setInputNormals</span> (cloud_normals2);        <span class="comment">// 输入对应法线</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 执行圆柱分割</span></span><br><span class="line">  seg.<span class="built_in">segment</span> (*inliers_cylinder, *coefficients_cylinder); <span class="comment">// 获取圆柱内点和参数</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;Cylinder coefficients: &quot;</span> &lt;&lt; *coefficients_cylinder &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 提取圆柱上的点</span></span><br><span class="line">  extract.<span class="built_in">setInputCloud</span> (cloud_filtered2);     <span class="comment">// 输入是非平面点云</span></span><br><span class="line">  extract.<span class="built_in">setIndices</span> (inliers_cylinder);       <span class="comment">// 使用圆柱内点索引</span></span><br><span class="line">  extract.<span class="built_in">setNegative</span> (<span class="literal">false</span>);                 <span class="comment">// 提取内点</span></span><br><span class="line">  pcl::PointCloud&lt;PointT&gt;::<span class="function">Ptr <span class="title">cloud_cylinder</span> <span class="params">(<span class="keyword">new</span> pcl::PointCloud&lt;PointT&gt; ())</span></span>;</span><br><span class="line">  extract.<span class="built_in">filter</span> (*cloud_cylinder);            <span class="comment">// 得到圆柱点云</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 判断是否找到圆柱</span></span><br><span class="line">  <span class="keyword">if</span> (cloud_cylinder-&gt;points.<span class="built_in">empty</span> ()) </span><br><span class="line">    std::cerr &lt;&lt; <span class="string">&quot;Can&#x27;t find the cylindrical component.&quot;</span> &lt;&lt; std::endl;</span><br><span class="line">  <span class="keyword">else</span></span><br><span class="line">  &#123;</span><br><span class="line">    std::cerr &lt;&lt; <span class="string">&quot;PointCloud representing the cylindrical component: &quot;</span> &lt;&lt; cloud_cylinder-&gt;points.<span class="built_in">size</span> () &lt;&lt; <span class="string">&quot; data points.&quot;</span> &lt;&lt; std::endl;</span><br><span class="line">    writer.<span class="built_in">write</span> (<span class="string">&quot;table_scene_mug_stereo_textured_cylinder.pcd&quot;</span>, *cloud_cylinder, <span class="literal">false</span>); <span class="comment">// 保存结果</span></span><br><span class="line">  &#125;</span><br><span class="line"></span><br><span class="line">  <span class="keyword">return</span> (<span class="number">0</span>); <span class="comment">// 成功退出</span></span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>

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href="#%E5%9C%86%E6%9F%B1%E4%BD%93%E6%A8%A1%E5%9E%8B%E5%88%86%E5%89%B2"><span class="toc-number">1.</span> <span class="toc-text">圆柱体模型分割</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%94%8D-1-%E6%95%B4%E4%BD%93%E6%B5%81%E7%A8%8B"><span class="toc-number">1.1.</span> <span class="toc-text">🔍 1. 整体流程</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%A7%A9-2-%E6%A0%B8%E5%BF%83%E7%BB%84%E4%BB%B6%E8%AF%B4%E6%98%8E"><span class="toc-number">1.2.</span> <span class="toc-text">🧩 2. 核心组件说明</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E2%9A%99%EF%B8%8F-3-%E5%B9%B3%E9%9D%A2%E5%88%86%E5%89%B2%E5%8F%82%E6%95%B0"><span class="toc-number">1.3.</span> <span class="toc-text">⚙️ 3. 平面分割参数</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%93%A6-4-%E5%9C%86%E6%9F%B1%E5%88%86%E5%89%B2%E9%9A%BE%E7%82%B9%E4%B8%8E%E5%AF%B9%E7%AD%96"><span class="toc-number">1.4.</span> <span class="toc-text">📦 4. 圆柱分割难点与对策</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%92%BE-5-%E8%BE%93%E5%87%BA%E7%BB%93%E6%9E%9C"><span class="toc-number">1.5.</span> <span class="toc-text">💾 5. 输出结果</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%93%8C-6-%E5%BA%94%E7%94%A8%E5%9C%BA%E6%99%AF"><span class="toc-number">1.6.</span> <span class="toc-text">📌 6. 应用场景</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%F0%9F%9B%A0%EF%B8%8F-7-%E5%8F%AF%E6%94%B9%E8%BF%9B%E6%96%B9%E5%90%91"><span class="toc-number">1.7.</span> <span class="toc-text">🛠️ 7. 可改进方向</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.8.</span> <span class="toc-text">代码实现</span></a></li></ol></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas 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